How do you interpret this learning curve?
Depends on what does 1 represent in your task. 
If you are trying to predict household prices and 1 represents \$1, I think the average validation loss is good. If 1 represents \$10000 in this case, probably something is not right. But remember that there are 2 parts contributing to the overall loss. The mse loss and the l2 penalty loss. (Also remember that most optimizers already implement l2 penalty as weight decay. So you do not need to add it separately)

Some suggestions.


Check if your data has any outliers/ anomalies. Based on your task you should know as to what you can do with these data points. Also see if your dataset has high variance. 
If you are worried about over-fitting, think about your data again. Less data + more parameters often leads to over-fitting. If your dataset is too small, you need to think again.  
Try to adjust the number of hidden units and observe the results.  
Try using cross validation.  
Alternatively, try using different optimizers and see what happens (Try Adam).